For the past several decades passive sonar signals have been processed by the human operator using visual and/or auditory displays to achieve detection and classification of target vessels. Recently, a small number of studies have begun to apply neural network techniques to this domain of signal processing. Time series data presents challenges to these pattern recognition networks and preprocessing methods are often critical. In this paper, a passive sonar hydrophone simulation is outlined. Methods and results of applying the backpropagation algorithm with a 3-layer feedforward network to simulated passive sonar data are presented. In particular, different types of fourier preprocessing and network parameters are examined for their effects on convergence rate (learning speed) and classification performance. Finally, the extension of this work to real passive sonar data and potential pitfalls are discussed.

Report Number

DREA-TC-93-305-VOL-1-P-105 — CONTAINED IN 93-02664

Date of publication

01 Feb 1993

Number of Pages

23

DSTKIM No

93-02658

CANDIS No

131303

Format(s):

Microfiche filmed at DSIS;Originator's fiche received by DSIS;Document Image stored on Optical Disk